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list_grok_bot_conversations

List THIS operator's Grok Bot desktop chat seats (not Terminal/ACP coding Groks) with live/quiet/gone labels and last turns. Use when they ask to see or talk about their Grok Bots. Omit seat to list; pass a slug for one seat. Send uses create_attention_directive even if the seat is quiet — the sticky waits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seatNoOptional slug or session id (fos-integrator or grok-bot-fos-integrator). Omit to list.
limitNoMax seats (default 8) or max turns when seat is set (default 6).
companyIdNoFreedomOS company id to act within (you must be a member). Required for company-scoped tools.

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden. It discloses that the tool returns labels and last turns, that omitting seat lists all vs passing slug yields one seat, and advises that even quiet seats can trigger a create_attention_directive. This adds meaningful behavioral context beyond what the schema provides. Minor gap: no mention of side effects, permissions, or empty state behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences with no redundancy. It front-loads the primary purpose, directly follows with usage context, and ends with a practical behavioral note. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description could detail the return format more explicitly, but it does state 'with live/quiet/gone labels and last turns'. The tool has 3 optional parameters and clear usage modes. The description adequately covers what an agent needs to select and invoke the tool, though error scenarios are omitted.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, baseline 3. The description adds value by explaining the seat parameter's dual behavior (list all vs single) and hinting at limit's dual purpose (max seats vs max turns). This meaningfully extends the schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists 'Grok Bot desktop chat seats' with 'live/quiet/gone labels and last turns'. It explicitly distinguishes from 'Terminal/ACP coding Groks', uses a specific verb+resource combination, and provides two usage modes (omit seat to list all, pass slug for one seat). This fully clarifies purpose and differentiates from sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit context: 'Use when they ask to see or talk about their Grok Bots.' It also instructs how to use the seat parameter and mentions a follow-up action (use create_attention_directive). While it doesn't exhaustively compare to all sibling list tools, the domain-specificity is strong and practical.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.6/5.0
Disambiguation4/5

The tool set is heavily disambiguated by detailed routing descriptions, domain prefixes, and lifecycle verbs, so most tools have a clear intended purpose. However, at 297 tools there are still close pairs and overlapping decision surfaces (e.g., approval workflows, 'what should I work on' readers, multiple finance/ads readers) that require careful description reading to avoid misselection.

Naming Consistency4/5

Naming is predominantly consistent snake_case verb_noun with strong domain prefixes like shopify_, x_, posthog_, and list_/create_/update_ patterns. Minor inconsistencies exist, such as several collection-returning tools using get_ (get_team_members, get_icps, get_okrs) instead of list_, and some generate_ vs create_ vs draft_ verbs, but the pattern is still predictable overall.

Tool Count1/5

297 tools is an extreme outlier and far beyond a usable MCP tool surface. Even a large suite has no justification for this count in one server; the agent would struggle to select among hundreds of similarly descriptive tools, and the natural 3-15 tool range is exceeded by nearly 20x.

Completeness4/5

The individual domains represented — OKRs, CRM/leads, Shopify, content pipelines, ads, PostHog, team hiring, knowledge, finance, and session management — are covered remarkably well with full lifecycle patterns. Minor gaps exist, such as no full deal CRUD, no delete for several Google/Shopify artifacts, and some analytical surfaces being read-heavy, but most workflows can be completed without dead ends.

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